Although there is no strict definition of data efficiency, it has been studied under various contexts [1]}, [2]}, [3]}, [4]}. As described in Section  of our main text, the requirement of more training data brings two issues: (a) more human labors are needed to collect and label enough training data; (b) more computational costs are required to train the model. In this paper, we aim to alleviate both these two issues of existing detection transformers. Thus, most of our experiments are performed on small-size datasets and implemented with a short training schedule of 50 epochs.
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